Read e-book online Advances in Imaging and Electron Physics, Vol. 124 PDF
By Peter W. Hawkes
The 4 surveys contained during this quantity illustrate broadly varied points of imaging and electron physics. the 1st chapters tackle purposes of electron microscopy, together with the function of this method in mineralogy and a examine of the excessive- answer electron microscopy of quasicrystals. The latter is absolutely a quick monograph offering the underlying crystallography and explaining intimately how the corresponding photographs and diffraction styles may be interpreted. The 3rd contribution is an account of a brand new method of deconvolution in picture processing within which the writer, by way of associating a polynomial with the gray-level values of (discrete) photos, exhibits that it truly is attainable to invert the convolutional relation that describes many varieties of snapshot formation. the amount ends with an in depth dialogue of the twin de Broglie wave, together with a serious account of recent advancements within the debate over the lifestyles and function of this sort of wave.
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Additional info for Advances in Imaging and Electron Physics, Vol. 124
In particular a direct dependency of the forward a priori prediction error at time n from the input sample at the same time makes the algorithm suitable for the ADPCM application in signal coding, as is shown in Section VI, even if this direct dependency is paid for with a structure that cannot be pipelined because of the presence of sums of products. V-VECTOR ALGEBRA AND VOLTERRA FILTERS 39 TABLE 1 Algorithm for Computing the y = Tx Producta z=0 From the last right column of V-vector to the ﬁrst From the ith row to the (i + L − 1) MOD L or from the ﬁrst to last row, if the column has less than i elements Compute: z hk = z + x hk dhk z = z hk From the ﬁrst right column of V-vector to the last From the (i − 1)th row to the (i − L) MOD L or from the last to ﬁrst row, if the column has less than i elements Compute: cdhk lhk = 1 + (cdhk )dhk −1/2 yhk = lhk (x hk + (cdhk )z hk ) c = c/lhk a TT T = I + cddT , and TT is a row i MOD L LUT II V-matrix.
In fast RLS algorithms, instabilities may appear because the Kalman gain vector, derived directly from Ωn , at a certain moment cannot be associated with any positivedeﬁnite autocorrelation matrix. However, in SQR algorithms, this problem is avoided by directly updating a SQR factor of Ωn or a quantity related to this 30 CARINI ET AL. factor. In fact, in this way, we implicitly impose the symmetry and positive deﬁniteness of the autocorrelation matrix. However, the SQR technique alone is not sufﬁcient to achieve the numerical stability of the algorithm, which also depends on the numerically robust computation of each algorithm parameter.
The important result is that the nonlinear channels are equalized with linear FIR ﬁlters. This fact can be justiﬁed intuitively because the vector equalizer can be seen as a beam former which, because of its diversity, is capable of nulling the nonlinearities and equalizing the linear part. Finally, a diagonal coordinate representation for Volterra ﬁlters which presents some analogies with V-vector notation was developed by Raz and Van Veen (1998). The diagonal coordinate representation allows a truncated Volterra ﬁlter to be characterized as a bank of linear ﬁlters whose coefﬁcients are deﬁned by the diagonal entries of its kernels.
Advances in Imaging and Electron Physics, Vol. 124 by Peter W. Hawkes